{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T05:23:44Z","timestamp":1769750624364,"version":"3.49.0"},"reference-count":12,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2017,12,20]],"date-time":"2017-12-20T00:00:00Z","timestamp":1513728000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,12,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Healthcare is one of the world\u2019s fastest growing industries, having large volumes of data collected on a daily basis. It is generally perceived as being \u2018information rich\u2019 yet \u2018knowledge poor\u2019. Hidden relationships and valuable knowledge can be discovered in the collected data from the application of data mining techniques. These techniques are being increasingly implemented in healthcare organizations in order to respond to the needs of doctors in their daily decision-making activities. To help the decision-makers to take the best decision it is fundamental to develop a solution able to predict events before their occurrence. The aim of this project was to predict if a patient would need to be followed by a nutrition specialist, by combining a nutritional dataset with data mining classification techniques, using WEKA machine learning tools. The achieved results showed to be very promising, presenting accuracy around 91%, specificity around 97% and precision about 95%.<\/jats:p>","DOI":"10.1515\/comp-2017-0008","type":"journal-article","created":{"date-parts":[[2018,1,16]],"date-time":"2018-01-16T17:16:29Z","timestamp":1516122989000},"page":"41-45","source":"Crossref","is-referenced-by-count":20,"title":["Machine Learning in Nutritional Follow-up Research"],"prefix":"10.1515","volume":"7","author":[{"given":"Rita","family":"Reis","sequence":"first","affiliation":[{"name":"University of Minho, Campus Gualtar, Braga 4710, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hugo","family":"Peixoto","sequence":"additional","affiliation":[{"name":"Algoritmi Research Center, University of Minho, Campus Gualtar, Braga 4710, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9","family":"Machado","sequence":"additional","affiliation":[{"name":"Algoritmi Research Center, University of Minho, Campus Gualtar, Braga 4710, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ant\u00f3nio","family":"Abelha","sequence":"additional","affiliation":[{"name":"Algoritmi Research Center, University of Minho, Campus Gualtar, Braga 4710, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2017,12,29]]},"reference":[{"key":"2025111020094136359_j_comp-2017-0008_ref_001_w2aab3b7c16b1b6b1ab1ab1Aa","doi-asserted-by":"crossref","unstructured":"[1] Portela, F., Santos, M. F., Machado, J., Abelha, A., Rua, F., & Silva, \u00c1. (2015). Real-time decision support using data mining to predict blood pressure critical events in intensive medicine patients. In Ambient Intelligence for Health (pp. 77-90). Springer International Publishing.10.1007\/978-3-319-26508-7_8","DOI":"10.1007\/978-3-319-26508-7_8"},{"key":"2025111020094136359_j_comp-2017-0008_ref_002_w2aab3b7c16b1b6b1ab1ab2Aa","unstructured":"[2] Abirami, N., Kamalakannan, T., & Muthukumaravel, A. (2013). A Study on Analysis of Various Data Mining Classification Techniques on Healthcare Data. International Journal of Emerging Technology and Advanced Engineering, 3(7), 604-607."},{"key":"2025111020094136359_j_comp-2017-0008_ref_003_w2aab3b7c16b1b6b1ab1ab3Aa","unstructured":"[3] Srinivas, K., Rani, B. K., & Govrdhan, A. (2010). Applications of data mining techniques in healthcare and prediction of heart attacks. International Journal on Computer Science and Engineering (IJCSE), 2(02), 250-255."},{"key":"2025111020094136359_j_comp-2017-0008_ref_004_w2aab3b7c16b1b6b1ab1ab4Aa","doi-asserted-by":"crossref","unstructured":"[4] Reis, R., Mendon\u00e7a, A., Ferreira, D. L. A., Peixoto, H.,&Machado, J. (2017). Business Intelligence for Nutrition Therapy. In Next- GenerationMobile and Pervasive Healthcare Solutions (pp. 203- 218). IGI Global.","DOI":"10.4018\/978-1-5225-2851-7.ch013"},{"key":"2025111020094136359_j_comp-2017-0008_ref_005_w2aab3b7c16b1b6b1ab1ab5Aa","unstructured":"[5] Eapen, A. G. (2004). Application of Data mining in Medical Applications."},{"key":"2025111020094136359_j_comp-2017-0008_ref_006_w2aab3b7c16b1b6b1ab1ab6Aa","unstructured":"[6] Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AImagazine, 17(3), 37."},{"key":"2025111020094136359_j_comp-2017-0008_ref_007_w2aab3b7c16b1b6b1ab1ab7Aa","unstructured":"[7] Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann."},{"key":"2025111020094136359_j_comp-2017-0008_ref_008_w2aab3b7c16b1b6b1ab1ab8Aa","unstructured":"[8] Han, J., Kamber, M., 2001. Data Mining: Concepts and Techniques, Morgan Kaufmann, San Fco., CA., USA."},{"key":"2025111020094136359_j_comp-2017-0008_ref_009_w2aab3b7c16b1b6b1ab1ab9Aa","unstructured":"[9] Shafique, U., & Qaiser, H. (2014). A comparative study of data mining process models (KDD, CRISP-DM and SEMMA). Int. J. Innov. Sci. Res, 12(1), 217-222."},{"key":"2025111020094136359_j_comp-2017-0008_ref_010_w2aab3b7c16b1b6b1ab1ac10Aa","doi-asserted-by":"crossref","unstructured":"[10] Milovic, B.,&Milovic, M. (2012). Prediction and decisionmaking in health care using data mining. Kuwait Chapter of the Arabian Journal of Business and Management Review, 1(12), 126.","DOI":"10.11591\/ijphs.v1i2.1380"},{"key":"2025111020094136359_j_comp-2017-0008_ref_011_w2aab3b7c16b1b6b1ab1ac11Aa","unstructured":"[11] Ferreira, P. M. S. (2010). Aplica\u00e7\u00e3o de Algoritmos de Aprendizagem Autom\u00e1tica para a Previs\u00e3o de Cancro de Mama (Master Thesis). Faculdade de Ci\u00eancias da Universidade do Porto, Porto, Portugal."},{"key":"2025111020094136359_j_comp-2017-0008_ref_012_w2aab3b7c16b1b6b1ab1ac12Aa","doi-asserted-by":"crossref","unstructured":"[12] Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd international conference on Machine learning (pp. 233-240). 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